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Liu Y, Huang S, Dong G, Hou C, Zhao Y, Zhang D. Computational identification of DNA damage-relevant lncRNAs for predicting therapeutic efficacy and clinical outcomes in cancer. Comput Biol Med 2024; 171:108107. [PMID: 38412692 DOI: 10.1016/j.compbiomed.2024.108107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/12/2024] [Accepted: 02/04/2024] [Indexed: 02/29/2024]
Abstract
OBJECTIVES The role of long non-coding RNAs (lncRNAs) in cancer treatment, particularly in modulating DNA repair programs, is an emerging field that warrants systematic exploration. This study aimed to systematically identify the lncRNA regulators that potentially regulate DNA damage response (DDR). METHODS Using genome-wide mRNA and lncRNA expression profiles of the same tumor patients, we proposed a novel computational framework. This framework performed Gene Set Variation Analysis to calculate DDR pathway enrichment score, which relies on weighting by tumor purity to obtain DDR activity score for each patient. Then, an in-depth differential expression profiling was conducted to identify pathway activity lncRNAs between high- and low-activity groups, utilizing a bootstrap-based randomization method. RESULTS We comprehensively charted the landscape of DDR-relevant lncRNAs across 23 epithelial-based cancer types. Its effectiveness was validated by assessing the consistency of these lncRNAs within various datasets of the same cancer type (hypergeometric test P < 0.001), examining the expression perturbation of these lncRNAs in response to treatment and demonstrating its application in prioritizing clinically-related lncRNAs. Furthermore, leveraging 820 epithelial ovarian cancer patients from four public datasets, we applied these lncRNAs identified by DDRLnc to establish and validate a risk stratification model to evaluate the benefits of platinum-based adjuvant chemotherapy for the improvement of clinical treatment outcomes. CONCLUSIONS Comprehensive pan-cancer analysis illustrates the utility of computational framework in identifying potentially lncRNAs involved in DDR, thereby offering novel insights into the complex realm of cancer research, and it will become a valuable tool for identifying potential therapeutic targets for cancer.
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Affiliation(s)
- Yixin Liu
- Modern Education Technology Center, Harbin Medical University, Harbin, 150080, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Guanghui Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China
| | - Chang Hou
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China
| | - Yuming Zhao
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150007, China.
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Schmutz M, Zucknick M, Schlenk RF, Mertens D, Benner A, Weichenhan D, Mücke O, Döhner K, Plass C, Bullinger L, Claus R. Predictive value of DNA methylation patterns in AML patients treated with an azacytidine containing induction regimen. Clin Epigenetics 2023; 15:171. [PMID: 37885041 PMCID: PMC10601277 DOI: 10.1186/s13148-023-01580-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Acute myeloid leukemia (AML) is a heterogeneous disease with a poor prognosis. Dysregulation of the epigenetic machinery is a significant contributor to disease development. Some AML patients benefit from treatment with hypomethylating agents (HMAs), but no predictive biomarkers for therapy response exist. Here, we investigated whether unbiased genome-wide assessment of pre-treatment DNA-methylation profiles in AML bone marrow blasts can help to identify patients who will achieve a remission after an azacytidine-containing induction regimen. RESULTS A total of n = 155 patients with newly diagnosed AML treated in the AMLSG 12-09 trial were randomly assigned to a screening and a refinement and validation cohort. The cohorts were divided according to azacytidine-containing induction regimens and response status. Methylation status was assessed for 664,227 500-bp-regions using methyl-CpG immunoprecipitation-seq, resulting in 1755 differentially methylated regions (DMRs). Top regions were distilled and included genes such as WNT10A and GATA3. 80% of regions identified as a hit were represented on HumanMethlyation 450k Bead Chips. Quantitative methylation analysis confirmed 90% of these regions (36 of 40 DMRs). A classifier was trained using penalized logistic regression and fivefold cross validation containing 17 CpGs. Validation based on mass spectra generated by MALDI-TOF failed (AUC 0.59). However, discriminative ability was maintained by adding neighboring CpGs. A recomposed classifier with 12 CpGs resulted in an AUC of 0.77. When evaluated in the non-azacytidine containing group, the AUC was 0.76. CONCLUSIONS Our analysis evaluated the value of a whole genome methyl-CpG screening assay for the identification of informative methylation changes. We also compared the informative content and discriminatory power of regions and single CpGs for predicting response to therapy. The relevance of the identified DMRs is supported by their association with key regulatory processes of oncogenic transformation and support the idea of relevant DMRs being enriched at distinct loci rather than evenly distribution across the genome. Predictive response to therapy could be established but lacked specificity for treatment with azacytidine. Our results suggest that a predictive epigenotype carries its methylation information at a complex, genome-wide level, that is confined to regions, rather than to single CpGs. With increasing application of combinatorial regimens, response prediction may become even more complicated.
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Affiliation(s)
- Maximilian Schmutz
- Hematology and Oncology, Medical Faculty, University of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Manuela Zucknick
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Richard F Schlenk
- NCT-Trial Center, National Center of Tumor Diseases, German Cancer Research Center, Heidelberg University Hospital, Heidelberg, Germany
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | - Daniel Mertens
- Cooperation Unit "Mechanisms of Leukemogenesis", German Cancer Research Center, Heidelberg, Germany
- Division of Chronic Lymphocytic Leukemia, Department of Internal Medicine III, Ulm University Medical Center, Ulm, Germany
| | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dieter Weichenhan
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Mücke
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Konstanze Döhner
- Department of Internal Medicine III, University Hospital of Ulm, Ulm, Germany
| | - Christoph Plass
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lars Bullinger
- German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany
- Department of Hematology, Oncology, and Cancer Immunology, Campus Virchow Klinikum, Berlin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rainer Claus
- Hematology and Oncology, Medical Faculty, University of Augsburg, Stenglinstr. 2, 86156, Augsburg, Germany.
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Pathology, Medical Faculty, University of Augsburg, Augsburg, Germany.
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Batis N, Brooks JM, Payne K, Sharma N, Nankivell P, Mehanna H. Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation. Adv Drug Deliv Rev 2021; 176:113854. [PMID: 34192550 PMCID: PMC8448142 DOI: 10.1016/j.addr.2021.113854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 12/30/2022]
Abstract
Predictive tools, utilising biomarkers, aim to objectively assessthe potentialresponse toa particular clinical intervention in order to direct treatment.Conventional cancer therapy remains poorly served by predictive biomarkers, despite being the mainstay of treatment for most patients. In contrast, targeted therapy benefits from a clearly defined protein target for potential biomarker assessment. We discuss potential data sources of predictive biomarkers for conventional and targeted therapy, including patient clinical data andmulti-omicbiomarkers (genomic, transcriptomic and protein expression).Key examples, either clinically adopted or demonstrating promise for clinical translation, are highlighted. Following this, we provide an outline of potential barriers to predictive biomarker development; broadly discussing themes of approaches to translational research and study/trial design, and the impact of cellular and molecular tumor heterogeneity. Future avenues of research are also highlighted.
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Affiliation(s)
- Nikolaos Batis
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.
| | - Jill M Brooks
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Karl Payne
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Neil Sharma
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom; Department of Head and Neck Surgery, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom
| | - Paul Nankivell
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom; Department of Head and Neck Surgery, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom
| | - Hisham Mehanna
- Institute of Head and Neck Studies and Education (InHANSE), Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom; Department of Head and Neck Surgery, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom.
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He L, Yang H, Huang J. The tumor immune microenvironment and immune-related signature predict the chemotherapy response in patients with osteosarcoma. BMC Cancer 2021; 21:581. [PMID: 34016089 PMCID: PMC8138974 DOI: 10.1186/s12885-021-08328-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/07/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Genome-wide expression profiles have been shown to predict the response to chemotherapy. The purpose of this study was to develop a novel predictive signature for chemotherapy in patients with osteosarcoma. METHODS We analysed the relevance of immune cell infiltration and gene expression profiles of the tumor samples of good responders with those of poor responders from the TARGET and GEO databases. Immune cell infiltration was evaluated using a single-sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm between good and poor chemotherapy responders. Differentially expressed genes were identified based on the chemotherapy response. LASSO regression and binary logistic regression analyses were applied to select the differentially expressed immune-related genes (IRGs) and developed a predictive signature in the training cohort. A receiver operating characteristic (ROC) curve analysis was employed to assess and validate the predictive accuracy of the predictive signature in the validation cohort. RESULTS The analysis of immune infiltration showed a positive relationship between high-level immune infiltration and good responders, and T follicular helper cells and CD8 T cells were significantly more abundant in good responders with osteosarcoma. Two hundred eighteen differentially expressed genes were detected between good and poor responders, and a five IRGs panel comprising TNFRSF9, CD70, EGFR, PDGFD and S100A6 was determined to show predictive power for the chemotherapy response. A chemotherapy-associated predictive signature was developed based on these five IRGs. The accuracy of the predictive signature was 0.832 for the training cohort and 0.720 for the validation cohort according to ROC analysis. CONCLUSIONS The novel predictive signature constructed with five IRGs can be effectively utilized to predict chemotherapy responsiveness and help improve the efficacy of chemotherapy in patients with osteosarcoma.
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Affiliation(s)
- Lijiang He
- Department of Orthopaedics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Hainan Yang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Jingshan Huang
- Department of General Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
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Theilhaber J, Chiron M, Dreymann J, Bergstrom D, Pollard J. Construction and optimization of gene expression signatures for prediction of survival in two-arm clinical trials. BMC Bioinformatics 2020; 21:333. [PMID: 32711453 PMCID: PMC7382041 DOI: 10.1186/s12859-020-03655-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 07/13/2020] [Indexed: 11/17/2022] Open
Abstract
Background Gene expression signatures for the prediction of differential survival of patients undergoing anti-cancer therapies are of great interest because they can be used to prospectively stratify patients entering new clinical trials, or to determine optimal treatment for patients in more routine clinical settings. Unlike prognostic signatures however, predictive signatures require training set data from clinical studies with at least two treatment arms. As two-arm studies with gene expression profiling have been rarer than similar one-arm studies, the methodology for constructing and optimizing predictive signatures has been less prominently explored than for prognostic signatures. Results Focusing on two “use cases” of two-arm clinical trials, one for metastatic colorectal cancer (CRC) patients treated with the anti-angiogenic molecule aflibercept, and the other for triple negative breast cancer (TNBC) patients treated with the small molecule iniparib, we present derivation steps and quantitative and graphical tools for the construction and optimization of signatures for the prediction of progression-free survival based on cross-validated multivariate Cox models. This general methodology is organized around two more specific approaches which we have called subtype correlation (subC) and mechanism-of-action (MOA) modeling, each of which leverage a priori knowledge of molecular subtypes of tumors or drug MOA for a given indication. The tools and concepts presented here include the so-called differential log-hazard ratio, the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curves and the patient selection matrix. In the CRC use case for instance, the resulting signature stratifies the patient population into “sensitive” and “relatively-resistant” groups achieving a more than two-fold difference in the aflibercept-to-control hazard ratios across signature-defined patient groups. Through cross-validation and resampling the probability of generalization of the signature to similar CRC data sets is predicted to be high. Conclusions The tools presented here should be of general use for building and using predictive multivariate signatures in oncology and in other therapeutic areas.
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Affiliation(s)
| | - Marielle Chiron
- Sanofi Oncology, Centre de Recherche de Vitry-Alfortville, 13 Quai Jules Guesde, 94400, Vitry-sur-Seine, France
| | - Jennifer Dreymann
- Sanofi Oncology, Centre de Recherche de Vitry-Alfortville, 13 Quai Jules Guesde, 94400, Vitry-sur-Seine, France
| | | | - Jack Pollard
- Sanofi Oncology, 270 Albany Street, Cambridge, MA, 02139, USA
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Nault JC, Galle PR, Marquardt JU. The role of molecular enrichment on future therapies in hepatocellular carcinoma. J Hepatol 2018; 69:237-47. [PMID: 29505843 DOI: 10.1016/j.jhep.2018.02.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 02/15/2018] [Accepted: 02/24/2018] [Indexed: 12/20/2022]
Abstract
Hepatocellular carcinomas (HCCs) are characterised by considerable phenotypic and molecular heterogeneity. Treating HCC and designing clinical trials are particularly challenging because co-existing liver disease, present in most patients, limits aggressive therapeutic options. Positive results in recent phase III clinical trials have confirmed the high value of anti-angiogenic therapies for HCC in both first (sorafenib and lenvatinib) and second line (regorafenib and cabozantinib) treatment modalities. However, failure of several large randomised controlled clinical trials over the last 10 years underlines the necessity for innovative treatment strategies and implementation of translational findings to overcome the unmet clinical need. Furthermore, the promising results from novel immunotherapies are likely to complement the landscape of active compounds for HCC and will require a completely different approach to patients, as well as the development of prognostic/predictive biomarkers. Given our increasing understanding of the most abundant molecular alterations in HCC, effective enrichment of patients based on clinical and molecular biomarkers, as well as adaptive clinical trials, are now feasible and should be implemented. Herein, we aim to review important aspects of precision medicine approaches in HCC that might contribute to improving the molecular subclassification of patients in a clinical trial setting and pave the way for novel therapeutic strategies.
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